[2603.29086] Realistic Market Impact Modeling for Reinforcement Learning Trading Environments

Computer Science > Machine Learning arXiv:2603.29086 (cs) [Submitted on 30 Mar 2026 (v1), last revised 4 Apr 2026 (this version, v2)] Title:Realistic Market Impact Modeling for Reinforcement Learning Trading Environments Authors:Lucas Riera Abbade, Anna Helena Reali Costa View a PDF of the paper titled Realistic Market Impact Modeling for Reinforcement Learning Trading Environments, by Lucas Riera Abbade and Anna Helena Reali Costa View PDF HTML (experimental) Abstract:Reinforcement learning (RL) has shown promise for trading, yet most open-source backtesting environments assume negligible or fixed transaction costs, causing agents to learn trading behaviors that fail under realistic execution. We introduce three Gymnasium-compatible trading environments -- MACE (Market-Adjusted Cost Execution) stock trading, margin trading, and portfolio optimization -- that integrate nonlinear market impact models grounded in the Almgren-Chriss framework and the empirically validated square-root impact law. Each environment provides pluggable cost models, permanent impact tracking with exponential decay, and comprehensive trade-level logging. We evaluate five DRL algorithms (A2C, PPO, DDPG, SAC, TD3) on the NASDAQ-100, comparing a fixed 10 bps baseline against the AC model with Optuna-tuned hyperparameters. Our results show that (i) the cost model materially changes both absolute performance and the relative ranking of algorithms across all three environments; (ii) the AC model produces ...